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Deep residual 2D convolutional neural network for cardiovascular disease classification
Haneen A Elyamani1, Mohammed A Salem2, Farid Melgani3
1Department of Mathematics, Faculty of Science, Suez Canal University, Ismailia, 44745, Egypt. hanen_yamany@science.suez.edu.eg.
Insights
A novel deep learning model for electrocardiogram (ECG) analysis shows high accuracy in detecting cardiovascular diseases (CVD). This AI-driven approach enhances diagnostic efficiency, improving accessibility to cardiac care.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Imaging Analysis
Background:
- Cardiovascular disease (CVD) remains a significant global health issue.
- Manual interpretation of electrocardiograms (ECGs) limits widespread diagnostic accessibility.
- Automated ECG analysis offers potential for improved accuracy and efficiency.
Purpose of the Study:
- To implement and evaluate a novel deep two-dimensional convolutional neural network (2D-CNN) for cardiac disorder detection using ECG data.
- To assess the performance of the 2D-CNN across different classification complexities (2, 5, and 23 cardiovascular disease classes).
Main Methods:
- A deep two-dimensional convolutional neural network (2D-CNN) was developed and applied to the PTB-XL dataset.
- The model was trained and validated for classifying cardiovascular conditions into 2, 5, and 23 distinct classes.
Main Results:
- The 2D-CNN achieved an Area Under the Curve (AUC) of 95% and 87.85% average accuracy for healthy/sick patient classification.
- In a 5-class classification, the model reached an AUC of 93.46% and 89.87% average accuracy.
- For 23-class classification, the model demonstrated an AUC of 92.18% and 96.88% accuracy, outperforming other methods on the same dataset.
Conclusions:
- The developed 2D-CNN model shows strong performance in classifying various cardiovascular diseases from ECGs.
- This AI-driven approach can assist healthcare professionals in clinical ECG analysis and computer-aided diagnosis.
- The findings suggest a potential for enhanced accessibility and accuracy in cardiovascular care.
Abstract:
Cardiovascular disease (CVD) continues to be a major global health concern, underscoring the need for advancements in medical care. The use of electrocardiograms (ECGs) is crucial for diagnosing cardiac conditions. However, the reliance on professional expertise for manual ECG interpretation poses challenges for expanding accessible healthcare, particularly in community hospitals. To address this, there is a growing interest in leveraging automated and AI-driven ECG analysis systems, which can enhance diagnostic accuracy and efficiency, making quality cardiac care more accessible to a broader population. In this study, we implemented a novel deep two-dimensional convolutional neural network (2D-CNN) on a dataset of PTB-XL for cardiac disorder detection. The studies were performed on 2, 5, and 23 classes of cardiovascular diseases. The our network in classifying healthy/sick patients achived an AUC of 95% and an average accuracy of 87.85%. In 5-classes classification, our model achieved an AUC of 93.46% with an average accuracy of 89.87%. In a more complex scenario involving classification into 23 different classes, the model achieved an AUC of 92.18% and an accuracy of 96.88%. According to the experimental results, our model obtained the best classification result compared to the other methods based on the same public dataset. This indicates that our method can aid healthcare professionals in the clinical analysis of ECGs, offering valuable assistance in diagnosing CVD and contributing to the advancement of computer-aided diagnosis technology.
